Mental models of driver monitoring systems: Perceptions of monitoring capabilities in an online U.S-based sample

Abstract

Driver monitoring, whether accompanying vehicle automation or not, appears poised for wide deployment in the near future. Driver monitoring is itself a form of automation (often needed in response to having automated other aspects of driving), and previous automation research has indicated that accurate mental models are critical to successful human-automation interactions. In this study, a descriptive survey of car owners examined perceived capabilities of generic driver monitoring systems (DMS) to detect 30 different potentially distracting behaviors or adverse mental states. Additional open-ended questions queried drivers about beliefs regarding how DMS work and how they might be defeated. Exploratory factor analysis revealed that perceived capabilities factored into four categories, including beliefs that driver monitoring systems can detect: (1) console and control interactions; (2) smartphone interactions; (3) gaze position and in-cabin movement; and (4) adverse mental states. Although research and industry analysis have suggested that camera-based gaze monitoring will be the path forward, a considerable minority of drivers appear to believe that console interactions will be monitored. Further, car owners in this study generally were skeptical about the ability DMS to detect adverse mental states, which may be in misalignment with the capabilities of systems that will be available in the near future. For future research, questions remain about the potential implications of false alarms (real and perceived) in driver monitoring, as well as the role of experience and initial training/instruction for deployed systems.